Dataset Information

Clinically Applicable Inhibitors Impacting Genome Stability.

ABSTRACT: Advances in technology have facilitated the molecular profiling (genomic and transcriptomic) of tumours, and has led to improved stratification of patients and the individualisation of treatment regimes. To fully realize the potential of truly personalised treatment options, we need targeted therapies that precisely disrupt the compensatory pathways identified by profiling which allow tumours to survive or gain resistance to treatments. Here, we discuss recent advances in novel therapies that impact the genome (chromosomes and chromatin), pathways targeted and the stage of the pathways targeted. The current state of research will be discussed, with a focus on compounds that have advanced into trials (clinical and pre-clinical). We will discuss inhibitors of specific DNA damage responses and other genome stability pathways, including those in development, which are likely to synergistically combine with current therapeutic options. Tumour profiling data, combined with the knowledge of new treatments that affect the regulation of essential tumour signalling pathways, is revealing fundamental insights into cancer progression and resistance mechanisms. This is the forefront of the next evolution of advanced oncology medicine that will ultimately lead to improved survival and may, one day, result in many cancers becoming chronic conditions, rather than fatal diseases.

SUBMITTER: Prakash A

PROVIDER: S-EPMC6100577 | biostudies-literature | 2018 May

REPOSITORIES: biostudies-literature

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Clinically Applicable Inhibitors Impacting Genome Stability.

Prakash Anu A Garcia-Moreno Juan F JF Brown James A L JAL Bourke Emer E

Molecules (Basel, Switzerland) 20180513 5

Advances in technology have facilitated the molecular profiling (genomic and transcriptomic) of tumours, and has led to improved stratification of patients and the individualisation of treatment regimes. To fully realize the potential of truly personalised treatment options, we need targeted therapies that precisely disrupt the compensatory pathways identified by profiling which allow tumours to survive or gain resistance to treatments. Here, we discuss recent advances in novel therapies that im ...[more]

PMID: 29757235

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Project description:BackgroundClassification of endometrial carcinomas (ECs) by morphologic features is inconsistent, and yields limited prognostic and predictive information. A new system for classification based on the molecular categories identified in The Cancer Genome Atlas is proposed.MethodsGenomic data from the Cancer Genome Atlas (TCGA) support classification of endometrial carcinomas into four prognostically significant subgroups; we used the TCGA data set to develop surrogate assays that could replicate the TCGA classification, but without the need for the labor-intensive and cost-prohibitive genomic methodology. Combinations of the most relevant assays were carried forward and tested on a new independent cohort of 152 endometrial carcinoma cases, and molecular vs clinical risk group stratification was compared.ResultsReplication of TCGA survival curves was achieved with statistical significance using multiple different molecular classification models (16 total tested). Internal validation supported carrying forward a classifier based on the following components: mismatch repair protein immunohistochemistry, POLE mutational analysis and p53 immunohistochemistry as a surrogate for 'copy-number' status. The proposed molecular classifier was associated with clinical outcomes, as was stage, grade, lymph-vascular space invasion, nodal involvement and adjuvant treatment. In multivariable analysis both molecular classification and clinical risk groups were associated with outcomes, but differed greatly in composition of cases within each category, with half of POLE and mismatch repair loss subgroups residing within the clinically defined 'high-risk' group. Combining the molecular classifier with clinicopathologic features or risk groups provided the highest C-index for discrimination of outcome survival curves.ConclusionsMolecular classification of ECs can be achieved using clinically applicable methods on formalin-fixed paraffin-embedded samples, and provides independent prognostic information beyond established risk factors. This pragmatic molecular classification tool has potential to be used routinely in guiding treatment for individuals with endometrial carcinoma and in stratifying cases in future clinical trials.

Project description:BackgroundSomatic copy number alterations (SCNAs) are an important class of genomic alteration in cancer. They are frequently observed in cancer samples, with studies showing that, on average, SCNAs affect 34% of a cancer cell's genome. Furthermore, SCNAs have been shown to be major drivers of tumour development and have been associated with response to therapy and prognosis. Large-scale cancer genome studies suggest that tumours are driven by somatic copy number alterations (SCNAs) or single-nucleotide variants (SNVs). Despite the frequency of SCNAs and their clinical relevance, the use of genomics assays in the clinic is biased towards targeted gene panels, which identify SNVs but provide limited scope to detect SCNAs throughout the genome. There is a need for a comparably low-cost and simple method for high-resolution SCNA profiling.ResultsWe present conliga, a fully probabilistic method that infers SCNA profiles from a low-cost, simple, and clinically-relevant assay (FAST-SeqS). When applied to 11 high-purity oesophageal adenocarcinoma samples, we obtain good agreement (Spearman's rank correlation coefficient, rs=0.94) between conliga's inferred SCNA profiles using FAST-SeqS data (approximately £14 per sample) and those inferred by ASCAT using high-coverage WGS (gold-standard). We find that conliga outperforms CNVkit (rs=0.89), also applied to FAST-SeqS data, and is comparable to QDNAseq (rs=0.96) applied to low-coverage WGS, which is approximately four-fold more expensive, more laborious and less clinically-relevant. By performing an in silico dilution series experiment, we find that conliga is particularly suited to detecting SCNAs in low tumour purity samples. At two million reads per sample, conliga is able to detect SCNAs in all nine samples at 3% tumour purity and as low as 0.5% purity in one sample. Crucially, we show that conliga's hidden state information can be used to decide when a sample is abnormal or normal, whereas CNVkit and QDNAseq cannot provide this critical information.ConclusionsWe show that conliga provides high-resolution SCNA profiles using a convenient, low-cost assay. We believe conliga makes FAST-SeqS a more clinically valuable assay as well as a useful research tool, enabling inexpensive and fast copy number profiling of pre-malignant and cancer samples.

Dataset Information

Clinically Applicable Inhibitors Impacting Genome Stability.

Publications

Clinically Applicable Inhibitors Impacting Genome Stability.

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